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Record W4405204327 · doi:10.1002/ajhb.24189

The Spiral of Attention, Arousal, and Release: A Comparative Phenomenology of Jhāna Meditation and Speaking in Tongues

2024· article· en· W4405204327 on OpenAlexafffund
Josh Brahinsky, Jonas Mago, Mark Miller, Shaila Catherine, Michael Lifshitz

Bibliographic record

VenueAmerican Journal of Human Biology · 2024
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsJewish General HospitalUniversity of TorontoMcGill University
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of CanadaJohn Templeton FoundationStudienstiftung des Deutschen VolkesMind and Life InstituteFundação BialNational Science Foundation
KeywordsMeditationPhenomenology (philosophy)ConsciousnessArousalPsychologyAltered statePsychology of selfCognitive psychologyAestheticsPsychoanalysisSocial psychologyEpistemologyPhilosophyTheologyNeuroscience

Abstract

fetched live from OpenAlex

Buddhist Jhāna meditation and the Christian practice of speaking in tongues appear wildly distinct. These spiritual techniques differ in their ethical, theological, and historical frames and seem, from the outside, to produce markedly different states of consciousness-one a state of utter calm and the other of high emotional arousal. Yet, our phenomenological interviews with experienced practitioners in the USA found significant points of convergence. Practitioners in both traditions describe a dynamic relationship between focused attention, aroused joy, and a sense of letting go or release that they describe as crucial to their practice. This paper highlights these shared phenomenological features and theorizes possible underlying mechanisms. Analyzing our phenomenological data through the lens of various theories of brain function, including sensory gating and predictive processing, we propose that these practices both engage an autonomic field built through a spiral between attention, arousal, and release (AAR).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.409
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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